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b/mains/main_VAE.py |
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#!/usr/bin/env python |
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import tensorflow as tf |
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from models import variational_autoencoder |
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from trainers.VAE import VAE |
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from utils import Evaluation |
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from utils.default_config_setup import get_config, get_options, get_datasets, Dataset |
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tf.reset_default_graph() |
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dataset = Dataset.BRAINWEB |
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options = get_options(batchsize=8, learningrate=0.0001, numEpochs=1, zDim=128, outputWidth=128, outputHeight=128) |
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options['data']['dir'] = options["globals"][dataset.value] |
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datasetHC, datasetPC = get_datasets(options, dataset=dataset) |
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config = get_config(trainer=VAE, options=options, optimizer='ADAM', intermediateResolutions=[8, 8], dropout_rate=0.1, dataset=datasetHC) |
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# Create an instance of the model and train it |
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model = VAE(tf.Session(), config, network=variational_autoencoder.variational_autoencoder) |
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# Train it |
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model.train(datasetHC) |
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# Evaluate |
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Evaluation.evaluate(datasetPC, model, options, description=f"{type(datasetHC).__name__}-{options['threshold']}", epoch=str(options['train']['numEpochs'])) |